Exploratory Data Analysis of Mutual Funds of India - March 2023¶

Dataset contains below columns

  • scheme_name: The name of the mutual fund scheme
  • min_sip: The minimum amount required for a Systematic Investment Plan (SIP) investment in the fund
  • min_lumpsum: The minimum amount required for a lump sum investment in the fund
  • expense_ratio: The expense ratio of the fund
  • fund_size_cr: The size of the fund in crore (10 million) units
  • fund_age_yr: The age of the fund in years
  • fund_manager: The name of the fund manager
  • sortino: The Sortino ratio of the fund, which measures the risk-adjusted return of the fund
  • alpha: The alpha of the fund, which measures the excess return of the fund compared to its benchmark
  • sd: The standard deviation of the fund's returns
  • beta: The beta of the fund, which measures the sensitivity of the fund's returns to the market
  • sharpe: The Sharpe ratio of the fund, which measures the risk-adjusted return of the fund relative to a risk-free asset
  • risk_level: The risk level of the fund, categorized as Very High,High, Moderately High, Moderately Low, or Low
  • amc_name: The name of the asset management company that manages the fund
  • rating: The rating of the fund on a scale of 1 to 5, with 1 being the lowest and 5 being the highest
  • category: The category of the fund, such as Equity, Debt, or Hybrid
  • sub_category: The sub-category of the fund, such as Large Cap, Small Cap, Floater Mutual Funds, etc.
  • returns_1yr: The return of the fund in the past year
  • returns_3yr: The return of the fund in the past 3 years
  • returns_5yr: The return of the fund in the past 5 years
In [1]:
# Importing several helpful packages & useful analytics libraries

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns

# loading the csv file
df= pd.read_csv(r"C:\Users\purna\Desktop\Projects\Data Analysis\Mutual fund insights\comprehensive_mutual_funds_data.csv",na_values='-')
df.head()
Out[1]:
scheme_name min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level amc_name rating category sub_category returns_1yr returns_3yr returns_5yr
0 Aditya Birla SL Active Debt Multi-Mgr FoF-Dir ... 100 100 0.27 10.0 10 Kaustubh Gupta 0.32 2.24 9.39 0.01 0.24 3 Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 4.0 6.5 6.9
1 Aditya Birla SL Arbitrage Fund 1000 1000 0.36 4288.0 10 Lovelish Solanki 1.33 1.53 0.72 0.56 1.10 1 Aditya Birla Sun Life Mutual Fund 3 Hybrid Arbitrage Mutual Funds 5.6 4.8 5.5
2 Aditya Birla SL Asset Allocator FoF-Dir Growth 1000 1000 0.53 157.0 10 Vinod Narayan Bhat 3.44 2.67 10.58 0.67 1.42 5 Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 2.0 18.9 9.7
3 Aditya Birla SL Bal Bhavishya Yojna – Dir Growth 500 1000 0.76 637.0 4 Atul Penkar 2.18 -6.37 14.99 0.85 0.90 6 Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Childrens Funds -0.7 17.1 NaN
4 Aditya Birla SL Balanced Advantage Fund 100 100 0.61 6386.0 10 Mohit Sharma 3.69 1.99 10.38 0.68 1.39 6 Aditya Birla Sun Life Mutual Fund 4 Hybrid Dynamic Asset Allocation or Balanced Advantage 4.5 18.6 9.7
In [2]:
# loading the csv file
df= pd.read_csv(r"C:\Users\purna\Desktop\Projects\Data Analysis\Mutual fund insights\comprehensive_mutual_funds_data.csv",na_values='-')
df.head()
Out[2]:
scheme_name min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level amc_name rating category sub_category returns_1yr returns_3yr returns_5yr
0 Aditya Birla SL Active Debt Multi-Mgr FoF-Dir ... 100 100 0.27 10.0 10 Kaustubh Gupta 0.32 2.24 9.39 0.01 0.24 3 Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 4.0 6.5 6.9
1 Aditya Birla SL Arbitrage Fund 1000 1000 0.36 4288.0 10 Lovelish Solanki 1.33 1.53 0.72 0.56 1.10 1 Aditya Birla Sun Life Mutual Fund 3 Hybrid Arbitrage Mutual Funds 5.6 4.8 5.5
2 Aditya Birla SL Asset Allocator FoF-Dir Growth 1000 1000 0.53 157.0 10 Vinod Narayan Bhat 3.44 2.67 10.58 0.67 1.42 5 Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 2.0 18.9 9.7
3 Aditya Birla SL Bal Bhavishya Yojna – Dir Growth 500 1000 0.76 637.0 4 Atul Penkar 2.18 -6.37 14.99 0.85 0.90 6 Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Childrens Funds -0.7 17.1 NaN
4 Aditya Birla SL Balanced Advantage Fund 100 100 0.61 6386.0 10 Mohit Sharma 3.69 1.99 10.38 0.68 1.39 6 Aditya Birla Sun Life Mutual Fund 4 Hybrid Dynamic Asset Allocation or Balanced Advantage 4.5 18.6 9.7
In [3]:
df['risk_level'].replace({1:'Low',2:'Moderately Low',3:'Moderate',4:'Moderately High',5:'High',6:'Very High'},inplace=True)
In [4]:
# checking the size of data (rows,columns)
df.shape
Out[4]:
(814, 20)
In [5]:
# checking for missing values and column datatypes are correct
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 814 entries, 0 to 813
Data columns (total 20 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   scheme_name    814 non-null    object 
 1   min_sip        814 non-null    int64  
 2   min_lumpsum    814 non-null    int64  
 3   expense_ratio  814 non-null    float64
 4   fund_size_cr   814 non-null    float64
 5   fund_age_yr    814 non-null    int64  
 6   fund_manager   814 non-null    object 
 7   sortino        791 non-null    float64
 8   alpha          772 non-null    float64
 9   sd             790 non-null    float64
 10  beta           772 non-null    float64
 11  sharpe         791 non-null    float64
 12  risk_level     814 non-null    object 
 13  amc_name       814 non-null    object 
 14  rating         814 non-null    int64  
 15  category       814 non-null    object 
 16  sub_category   814 non-null    object 
 17  returns_1yr    814 non-null    float64
 18  returns_3yr    793 non-null    float64
 19  returns_5yr    647 non-null    float64
dtypes: float64(10), int64(4), object(6)
memory usage: 127.3+ KB
In [6]:
# checking for missing values
df.isnull().sum()
Out[6]:
scheme_name        0
min_sip            0
min_lumpsum        0
expense_ratio      0
fund_size_cr       0
fund_age_yr        0
fund_manager       0
sortino           23
alpha             42
sd                24
beta              42
sharpe            23
risk_level         0
amc_name           0
rating             0
category           0
sub_category       0
returns_1yr        0
returns_3yr       21
returns_5yr      167
dtype: int64

We will try to find why sortino, alpha, sd,beta,share,returns_3yr and returns_5yr data is missing

In [7]:
# checking for duplicate rows if any
df.duplicated().sum()
Out[7]:
0
In [8]:
# Lets investigate why there are missing values in returns_3yr and returns_5yr
df[df['returns_3yr'].isnull()]
Out[8]:
scheme_name min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level amc_name rating category sub_category returns_1yr returns_3yr returns_5yr
61 AXIS Floater Fund 1000 5000 0.16 416.0 2 Aditya Pagaria NaN NaN NaN NaN NaN Moderately Low Axis Mutual Fund 2 Debt Floater Mutual Funds 5.8 NaN NaN
104 Baroda BNP Paribas Banking & PSU Bond Fund 500 5000 0.39 40.0 2 Prashant Pimple NaN NaN NaN NaN NaN Moderately Low Baroda BNP Paribas Mutual Fund 2 Debt Banking and PSU Mutual Funds 2.5 NaN NaN
125 BHARAT Bond FOF – April 2025 – Direct Growth 500 1000 0.06 3912.0 3 Dhawal Dalal NaN NaN NaN NaN NaN Moderately Low Edelweiss Mutual Fund 2 Other FoFs Domestic 2.6 NaN NaN
154 DSP Floater Fund 500 500 0.24 983.0 2 Kedar Karnik NaN NaN NaN NaN NaN Moderately Low DSP Mutual Fund 1 Debt Floater Mutual Funds 4.4 NaN NaN
398 Invesco India Medium Duration Fund 1000 1000 0.40 306.0 2 Vikas Garg NaN NaN NaN NaN NaN Moderate Invesco Mutual Fund 2 Debt Medium Duration Funds 3.5 NaN NaN
411 ITI Banking & PSU Debt Fund 500 5000 0.15 27.0 2 Vikrant Mehta NaN NaN NaN NaN NaN Moderately Low ITI Mutual Fund 5 Debt Banking and PSU Mutual Funds 5.4 NaN NaN
412 ITI Dynamic Bond Fund 500 5000 0.14 57.0 2 Vikrant Mehta NaN NaN NaN NaN NaN Moderately Low ITI Mutual Fund 5 Debt Dynamic Bond 5.7 NaN NaN
417 ITI Ultra Short Duration Fund 500 5000 0.10 119.0 2 Vikrant Mehta NaN NaN NaN NaN NaN Moderately Low ITI Mutual Fund 2 Debt Ultra Short Duration Funds 5.3 NaN NaN
495 Mahindra Manulife Short Duration Fund 500 1000 0.29 43.0 2 Rahul Pal NaN NaN NaN NaN NaN Moderate Mahindra Manulife Mutual Fund 3 Debt Short Duration Funds 4.4 NaN NaN
497 Mirae Asset Banking and PSU Debt Fund 1000 5000 0.34 89.0 3 Basant Bafna NaN NaN NaN NaN NaN Moderately Low Mirae Asset Mutual Fund 3 Debt Banking and PSU Mutual Funds 3.7 NaN NaN
499 Mirae Asset Corporate Bond Fund 1000 5000 0.46 57.0 2 Mahendra Jajoo NaN NaN NaN NaN NaN Moderately Low Mirae Asset Mutual Fund 3 Debt Corporate Bond Mutual Funds 3.7 NaN NaN
509 Mirae Asset Money Market Fund 1000 5000 0.25 59.0 2 Basant Bafna NaN NaN NaN NaN NaN Moderately Low Mirae Asset Mutual Fund 3 Debt Money Market Funds 5.3 NaN NaN
514 Mirae Asset Ultra Short Duration Fund 1000 5000 0.29 284.0 2 Basant Bafna NaN NaN NaN NaN NaN Moderately Low Mirae Asset Mutual Fund 3 Debt Ultra Short Duration Funds 5.5 NaN NaN
631 SBI Floating Rate Debt Fund 500 5000 0.26 1604.0 2 Rajeev Radhakrishnan NaN NaN NaN NaN NaN Moderately Low SBI Mutual Fund 3 Debt Floater Mutual Funds 5.0 NaN NaN
689 Sundaram Large Cap Fund 100 100 0.60 2855.0 2 Sudhir Kedia 3.64 -0.51 15.44 0.91 1.3 Very High Sundaram Mutual Fund 5 Equity Large Cap Mutual Funds 0.9 NaN NaN
720 Tata Floating Rate Fund 150 5000 0.30 225.0 2 Akhil Mittal NaN NaN NaN NaN NaN Moderately Low Tata Mutual Fund 4 Debt Floater Mutual Funds 5.6 NaN NaN
755 TRUSTMF Banking & PSU Debt Fund 1000 1000 0.21 345.0 2 Anand Nevatia NaN NaN NaN NaN NaN Moderate Trust Mutual Fund 2 Debt Banking and PSU Mutual Funds 3.0 NaN NaN
756 TRUSTMF Liquid Fund 1000 1000 0.10 205.0 2 Anand Nevatia NaN NaN NaN NaN NaN Moderately Low Trust Mutual Fund 2 Debt Liquid Mutual Funds 5.6 NaN NaN
757 TRUSTMF Short Term Fund 1000 1000 0.23 94.0 2 Anand Nevatia NaN NaN NaN NaN NaN Moderately Low Trust Mutual Fund 2 Debt Short Duration Funds 4.2 NaN NaN
769 Union Medium Duration Fund 500 1000 0.64 170.0 3 Parijat Agrawal NaN NaN NaN NaN NaN Moderate Union Mutual Fund 2 Debt Medium Duration Funds 2.3 NaN NaN
770 Union Money Market Fund 2000 5000 0.24 144.0 2 Parijat Agrawal NaN NaN NaN NaN NaN Moderately Low Union Mutual Fund 3 Debt Money Market Funds 5.4 NaN NaN

We find that there are missing values in sortino, alpha,beta,sd,sharpe, returns_3yr and returns_5yr since fund age is less than 3 years It seems that sortino, alpha,beta,sd,sharpe ratios are calculated considering 3 years data

In [9]:
df[df['returns_5yr'].isnull()]
Out[9]:
scheme_name min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level amc_name rating category sub_category returns_1yr returns_3yr returns_5yr
3 Aditya Birla SL Bal Bhavishya Yojna – Dir Growth 500 1000 0.76 637.0 4 Atul Penkar 2.18 -6.37 14.99 0.85 0.90 Very High Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Childrens Funds -0.7 17.1 NaN
38 Aditya Birla SL Overnight Fund 0 5000 0.07 13399.0 4 Kaustubh Gupta 0.11 0.58 0.35 0.43 0.02 Low Aditya Birla Sun Life Mutual Fund 3 Debt Overnight Mutual Funds 5.4 3.9 NaN
41 Aditya Birla SL Retirement-The 30s Plan-Dir Gr... 500 1000 1.06 258.0 4 Dhaval Shah 2.41 -5.61 14.94 0.84 0.94 Very High Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Retirement Funds 0.0 17.9 NaN
42 Aditya Birla SL Retirement-The 40s Plan-Dir Gr... 500 1000 1.07 91.0 4 Dhaval Shah 2.34 -3.14 11.86 0.77 0.93 Very High Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Retirement Funds -0.1 15.0 NaN
43 Aditya Birla SL Retirement-The 50s Plan-Dir Gr... 500 1000 0.63 27.0 4 Dhaval Shah 0.29 -2.04 2.99 0.34 0.13 Moderately High Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Retirement Funds 3.0 4.6 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
784 UTI-Equity Savings Fund 0 5000 0.68 257.0 5 V Srivatsa 4.06 3.98 5.99 0.91 1.75 Moderate UTI Mutual Fund 4 Hybrid Equity Savings Mutual Funds 6.1 14.7 NaN
786 UTI-Floater Fund 500 500 0.35 1637.0 4 Sudhir Agrawal 3.07 3.41 1.05 1.41 1.45 Moderately Low UTI Mutual Fund 3 Debt Floater Mutual Funds 5.1 5.9 NaN
801 UTI-Nifty Next 50 Index Fund 0 5000 0.33 1926.0 5 Sharwan Kumar Goyal 1.71 -4.62 18.16 0.99 0.97 Very High UTI Mutual Fund 2 Other Index Funds -6.4 22.2 NaN
811 WhiteOak Capital Liquid Fund 500 500 0.26 197.0 4 Piyush Baranwal -0.42 0.93 0.39 0.72 -0.07 Low WhiteOak Capital Mutual Fund 1 Debt Liquid Mutual Funds 5.3 3.8 NaN
813 WhiteOak Capital Ultra Short Term Fund 500 500 0.33 141.0 4 Piyush Baranwal 2.81 1.20 0.72 0.47 0.80 Low WhiteOak Capital Mutual Fund 2 Debt Ultra Short Duration Funds 5.3 4.5 NaN

167 rows × 20 columns

Clearly missing values in returns_5yr is there as fund age is not greater than 5 years

In [10]:
# mean returns across category
mean_returns_by_category = df.groupby('category')['returns_3yr','returns_5yr'].mean()
mean_returns_by_category

C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2654559691.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  mean_returns_by_category = df.groupby('category')['returns_3yr','returns_5yr'].mean()
Out[10]:
returns_3yr returns_5yr
category
Debt 6.228517 6.385446
Equity 29.738111 12.069767
Hybrid 15.272414 8.581053
Other 20.792405 10.710938
Solution Oriented 18.167857 9.747059
In [11]:
# mean performance ratios across category
mean_ratios_by_category = df.groupby('category')['sortino','alpha','sd','beta','sharpe'].mean()
mean_ratios_by_category

C:\Users\purna\AppData\Local\Temp\ipykernel_39816\4022942247.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  mean_ratios_by_category = df.groupby('category')['sortino','alpha','sd','beta','sharpe'].mean()
Out[11]:
sortino alpha sd beta sharpe
category
Debt 2.139885 2.160000 2.092107 0.807214 0.892366
Equity 3.110392 3.402550 16.959477 0.878642 1.459837
Hybrid 2.776552 2.486034 7.493190 0.707155 1.311638
Other 2.420633 0.513594 15.480506 0.850938 1.090253
Solution Oriented 2.589286 0.700357 10.272143 0.765714 1.204286
In [12]:
# imputing missing values with mean of category

df['returns_3yr'] = df.groupby('category')['returns_3yr'].apply(lambda x: x.fillna(x.mean()))
df['returns_5yr'] = df.groupby('category')['returns_5yr'].apply(lambda x: x.fillna(x.mean()))
df['sortino'] = df.groupby('category')['sortino'].apply(lambda x: x.fillna(x.mean()))
df['alpha'] = df.groupby('category')['alpha'].apply(lambda x: x.fillna(x.mean()))
df['sd'] = df.groupby('category')['sd'].apply(lambda x: x.fillna(x.mean()))
df['beta'] = df.groupby('category')['beta'].apply(lambda x: x.fillna(x.mean()))
df['sharpe'] = df.groupby('category')['sharpe'].apply(lambda x: x.fillna(x.mean()))

C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:3: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['returns_3yr'] = df.groupby('category')['returns_3yr'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:4: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['returns_5yr'] = df.groupby('category')['returns_5yr'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:5: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['sortino'] = df.groupby('category')['sortino'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:6: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['alpha'] = df.groupby('category')['alpha'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:7: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['sd'] = df.groupby('category')['sd'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:8: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['beta'] = df.groupby('category')['beta'].apply(lambda x: x.fillna(x.mean()))
C:\Users\purna\AppData\Local\Temp\ipykernel_39816\2214251339.py:9: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.
To preserve the previous behavior, use

	>>> .groupby(..., group_keys=False)

To adopt the future behavior and silence this warning, use 

	>>> .groupby(..., group_keys=True)
  df['sharpe'] = df.groupby('category')['sharpe'].apply(lambda x: x.fillna(x.mean()))
In [13]:
# verifying missing values are imputed
df.isnull().sum()
Out[13]:
scheme_name      0
min_sip          0
min_lumpsum      0
expense_ratio    0
fund_size_cr     0
fund_age_yr      0
fund_manager     0
sortino          0
alpha            0
sd               0
beta             0
sharpe           0
risk_level       0
amc_name         0
rating           0
category         0
sub_category     0
returns_1yr      0
returns_3yr      0
returns_5yr      0
dtype: int64
In [14]:
# checking statistics of numerical columns
df.describe()
Out[14]:
min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr sortino alpha sd beta sharpe rating returns_1yr returns_3yr returns_5yr
count 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000 814.000000
mean 528.500000 3047.468059 0.713231 3812.854214 8.319410 2.640883 2.464598 10.084476 0.822852 1.197011 2.641278 3.921376 18.254880 9.389900
std 369.184517 2514.619513 0.482161 7181.479475 2.640622 1.374909 3.859215 7.808093 1.275605 0.531163 1.463476 6.675502 12.101639 3.497044
min 0.000000 0.000000 0.000000 2.380000 1.000000 -1.770000 -13.360000 0.270000 -31.860000 -0.820000 0.000000 -19.700000 3.300000 -4.100000
25% 150.000000 500.000000 0.310000 175.250000 6.000000 1.810000 0.513594 2.092107 0.710000 0.894275 2.000000 1.500000 6.228517 6.389085
50% 500.000000 5000.000000 0.615000 940.500000 10.000000 2.825000 1.885000 11.870000 0.850938 1.290000 3.000000 4.400000 18.050000 8.800000
75% 1000.000000 5000.000000 1.030000 3819.000000 10.000000 3.460000 3.727500 16.410000 0.960000 1.540000 4.000000 5.600000 27.000000 12.069767
max 2000.000000 25000.000000 2.590000 57052.000000 17.000000 12.570000 27.240000 77.720000 5.400000 3.520000 5.000000 130.800000 71.400000 23.200000

There seems to be presence of outliers in columns min_lumpsum, fund_size_cr as mean and median differ significantly

In [15]:
df.head()
Out[15]:
scheme_name min_sip min_lumpsum expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level amc_name rating category sub_category returns_1yr returns_3yr returns_5yr
0 Aditya Birla SL Active Debt Multi-Mgr FoF-Dir ... 100 100 0.27 10.0 10 Kaustubh Gupta 0.32 2.24 9.39 0.01 0.24 Moderate Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 4.0 6.5 6.900000
1 Aditya Birla SL Arbitrage Fund 1000 1000 0.36 4288.0 10 Lovelish Solanki 1.33 1.53 0.72 0.56 1.10 Low Aditya Birla Sun Life Mutual Fund 3 Hybrid Arbitrage Mutual Funds 5.6 4.8 5.500000
2 Aditya Birla SL Asset Allocator FoF-Dir Growth 1000 1000 0.53 157.0 10 Vinod Narayan Bhat 3.44 2.67 10.58 0.67 1.42 High Aditya Birla Sun Life Mutual Fund 3 Other FoFs Domestic 2.0 18.9 9.700000
3 Aditya Birla SL Bal Bhavishya Yojna – Dir Growth 500 1000 0.76 637.0 4 Atul Penkar 2.18 -6.37 14.99 0.85 0.90 Very High Aditya Birla Sun Life Mutual Fund 2 Solution Oriented Childrens Funds -0.7 17.1 9.747059
4 Aditya Birla SL Balanced Advantage Fund 100 100 0.61 6386.0 10 Mohit Sharma 3.69 1.99 10.38 0.68 1.39 Very High Aditya Birla Sun Life Mutual Fund 4 Hybrid Dynamic Asset Allocation or Balanced Advantage 4.5 18.6 9.700000

Analysis on fund_manager¶

In [16]:
# fund manager with highest returns in 5 years

df.groupby(['fund_manager'])['returns_5yr'].mean().sort_values(ascending=False).nlargest(10).plot(kind='barh')
plt.title('Top 10 fund manager by 5 years returns')

plt.show()
In [17]:
# creating category wise dataframes

debt_df = df[df['category'] == 'Debt']
equity_df = df[df['category'] == 'Equity']
hybrid_df = df[df['category'] == 'Hybrid']
other_df = df[df['category'] == 'Other']
solution_df = df[df['category'] == 'Solution Oriented']
In [18]:
# calculating top 5 fund manager with avg highest returns in each category

top_5_debt = debt_df.groupby('fund_manager')['returns_5yr'].mean().sort_values(ascending=False).head().reset_index()
top_5_equity = equity_df.groupby('fund_manager')['returns_5yr'].mean().sort_values(ascending=False).head().reset_index()
top_5_hybrid = hybrid_df.groupby('fund_manager')['returns_5yr'].mean().sort_values(ascending=False).head().reset_index()
top_5_solution = solution_df.groupby('fund_manager')['returns_5yr'].mean().sort_values(ascending=False).head().reset_index()
top_5_other = other_df.groupby('fund_manager')['returns_5yr'].mean().sort_values(ascending=False).head().reset_index()
In [19]:
# create the plot
fig, ax = plt.subplots(nrows=1,ncols=5,figsize=(14, 4),sharey='row')
sns.barplot(x='fund_manager', y='returns_5yr', data=top_5_debt, ax=ax[0],color='#F1DC2F')
sns.barplot(x='fund_manager', y='returns_5yr', data=top_5_equity, ax=ax[1],color='#F1512F')
sns.barplot(x='fund_manager', y='returns_5yr', data=top_5_hybrid, ax=ax[2],color='#27AF17')
sns.barplot(x='fund_manager', y='returns_5yr', data=top_5_solution, ax=ax[3],color='#17AFA3')
sns.barplot(x='fund_manager', y='returns_5yr', data=top_5_other, ax=ax[4],color='#AF178A')

# set plot title and x labels
ax[0].set_title('Debt Funds Returns')
ax[0].set_xticklabels(top_5_debt['fund_manager'],rotation='vertical')
ax[1].set_title('Equity Funds Returns')
ax[1].set_xticklabels(top_5_equity['fund_manager'],rotation='vertical')
ax[2].set_title('Hybrid Funds Returns')
ax[2].set_xticklabels(top_5_hybrid['fund_manager'],rotation='vertical')
ax[3].set_title('Solution Funds Returns')
ax[3].set_xticklabels(top_5_solution['fund_manager'],rotation='vertical')
ax[4].set_title('Other Funds Returns')
ax[4].set_xticklabels(top_5_other['fund_manager'],rotation='vertical')

# show the plot
plt.tight_layout()
plt.suptitle('Top 5 fund manager in each category by average 5 year of returns')
plt.subplots_adjust(top=0.8)
plt.show()

Conclusion

  • There are 3 fund manager in Debt Funds having higher returns among top 5 fund manager.
  • In the Equity category Vaibhav Dusad has managed to give highest returns.
  • In the Hybrid category Vasav Sahgal has managed to give highest returns.
  • In the Solution Funds category Srinivasan Ramamurthy has managed to give highest returns.
  • In the Other Funds category Devender Singhal has managed to give highest returns.
In [20]:
# top 10 fund manager by the number of schemes

df['fund_manager'].value_counts().nlargest(10).plot(kind='barh')
plt.xlabel('Number of schemes')
plt.title('Top 10 fund manager by No of schemes')
plt.show()

Conclusion

  • Rohit Seksaria is managing the highest number of Funds.
In [21]:
# top 10 fund manager by the mean age of funds
df.groupby(['fund_manager'])['fund_age_yr'].mean().sort_values(ascending=False).nlargest(10).plot(kind='barh')
plt.xlabel('Age of funds in year')
plt.title('Top 10 fund manager by oldest schemes')
plt.show()

Conclusion

  • George Thomas is managing a fund which is the oldest among our dataset.
In [22]:
# top 10 fund manager by the assets under management
df.groupby(['fund_manager'])['fund_size_cr'].mean().sort_values(ascending=False).nlargest(10).plot(kind='barh')
plt.xlabel('Fund value in Crore')
plt.title('Top 10 fund manager having highest assets under management')
plt.show()

Conclusion

  • Anupam Joshi has the highest AUM.

Analysis on min_sip and min_lumpsum¶

In [23]:
# distribution of min_sip
fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(14, 4))

sns.histplot(df['min_sip'],ax=ax[0],kde=True)
sns.boxplot(df['min_sip'],ax=ax[1])
Out[23]:
<Axes: >

Conclusion

  • Most of the funds have Minimum SIP amount as 0,500 and 1000
In [24]:
# distribution of min_lumpsum
fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(14, 4))

sns.histplot(df['min_lumpsum'],ax=ax[0],kde=True)
sns.boxplot(df['min_lumpsum'],ax=ax[1])
Out[24]:
<Axes: >

Conclusion

  • Most of the funds have Minimum Lumpsum amount between 0 to 5000
In [25]:
# distribution of min_sip and min_lumpsum by category type
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 8))

sns.boxplot(data = df,y=df['category'],x=df['min_lumpsum'],ax=axes[0])
sns.boxplot(data = df,y=df['category'],x=df['min_sip'],ax=axes[1])

axes[0].set_title('Lumpsum investment required by Category type')
axes[1].set_title('SIP investment required by Category type')
plt.subplots_adjust(bottom=4,top=5)
plt.show()

Conclusion

  • Across fund type lumpum investment is almost same.
  • Across fund type sip investment is high among Hybrid and Debt funds.
In [26]:
# distribution of min_sip,min_lumpsum by sub_category type

fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(12, 16),sharey='row')

sns.boxplot(equity_df,y='sub_category',x='min_lumpsum',ax=axes[0,0])
sns.boxplot(hybrid_df,y='sub_category',x='min_lumpsum',ax=axes[1,0])
sns.boxplot(solution_df,y='sub_category',x='min_lumpsum',ax=axes[2,0])
sns.boxplot(other_df,y='sub_category',x='min_lumpsum',ax=axes[3,0])
sns.boxplot(debt_df,y='sub_category',x='min_lumpsum',ax=axes[4,0])

axes[0,0].set_title('Minimum Lumpsum Investment of Equity fund by sub-Category')
axes[1,0].set_title('Minimum Lumpsum Investment of Hybrid fund by sub-Category')
axes[2,0].set_title('Minimum Lumpsum Investment of Solution Oriented fund by sub-Category')
axes[3,0].set_title('Minimum Lumpsum Investment of Other fund by sub-Category')
axes[4,0].set_title('Minimum Lumpsum Investment of Debt fund by sub-Category')


sns.boxplot(equity_df,y='sub_category',x='min_sip',ax=axes[0,1])
sns.boxplot(hybrid_df,y='sub_category',x='min_sip',ax=axes[1,1])
sns.boxplot(solution_df,y='sub_category',x='min_sip',ax=axes[2,1])
sns.boxplot(other_df,y='sub_category',x='min_sip',ax=axes[3,1])
sns.boxplot(debt_df,y='sub_category',x='min_sip',ax=axes[4,1])

axes[0,1].set_title('Minimum SIP Investment of Equity fund by sub-Category')
axes[1,1].set_title('Minimum SIP Investment of Hybrid fund by sub-Category')
axes[2,1].set_title('Minimum SIP Investment of Solution Oriented fund by sub-Category')
axes[3,1].set_title('Minimum SIP Investment of Other fund by sub-Category')
axes[4,1].set_title('Minimum SIP Investment of Debt fund by sub-Category')

plt.subplots_adjust(bottom=2.5,top=4,left=2,right=3)
plt.show()

Analysis on expense ratio¶

  • The expense ratio measures how much of a fund's assets are used for administrative and other operating expenses.
  • A mutual fund expense ratio that is at or below its peer average is ideal.

image.png

In [27]:
# create a function to make a category columns with above fund size

def fund_size_group(fund_size):
    if fund_size >= 0.0 and fund_size <= 500.0:
        return '0-500'
    elif fund_size > 500.0 and fund_size <= 750.0:
        return '500-750'
    elif fund_size > 750.0 and fund_size <= 2000.0:
        return '750-2000'
    elif fund_size > 2000.0 and fund_size <= 5000.0:
        return '2000-5000'
    elif fund_size > 5000.0 and fund_size <= 10000.0:
        return '5000-10000'
    elif fund_size > 10000.0 and fund_size <= 50000.0:
        return '10000-50000'
    else:
        return '>50000'
In [28]:
# create a column with above fund size group
df['fund_size_group'] = df['fund_size_cr'].apply(fund_size_group)
In [29]:
# expense ratio variation with fund size group across different category

# Create the categorical plot with subplots
g = sns.catplot(data=df, x='fund_size_group', y='expense_ratio', kind='bar', col='category', col_wrap=3)

# Set the rotation of the x-axis tick labels for all subplots
g.set_xticklabels(rotation='vertical')

# set the super title
plt.suptitle('Expense Ratio variation across different Fund size group and category',fontsize=20)
plt.subplots_adjust(top=0.8)

# Show the plot
plt.show()

Findings:

  • Large fund size does have low expense ratio.

  • Debt has the lowest 'expense ratio' amongs the funds.

In [30]:
# expense ratio distribution
fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(14, 4))

sns.histplot(df['expense_ratio'],ax=ax[0],kde=True)
sns.boxplot(df['expense_ratio'],ax=ax[1])
Out[30]:
<Axes: >

Conclusion

  • Expense ratio of most of the schemes lies between 0 to 1
In [31]:
# distribution of expense ratio by category type
plt.figure(figsize=(10,8))
sns.boxplot(df,y=df['category'],x=df['expense_ratio'])
plt.show()

Conclusion

  • Equity and Solution oriented funds have the highest expense ratio among funds.
In [32]:
# distribution of expense ratio by sub-category type
fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(10, 16))

sns.boxplot(equity_df,y='sub_category',x='expense_ratio',ax=axes[0])
sns.boxplot(hybrid_df,y='sub_category',x='expense_ratio',ax=axes[1])
sns.boxplot(solution_df,y='sub_category',x='expense_ratio',ax=axes[2])
sns.boxplot(other_df,y='sub_category',x='expense_ratio',ax=axes[3])
sns.boxplot(debt_df,y='sub_category',x='expense_ratio',ax=axes[4])

axes[0].set_title('Expense Ratio of Equity fund by sub-Category type')
axes[1].set_title('Expense Ratio of Hybrid fund by sub-Category type')
axes[2].set_title('Expense Ratio of Solution Oriented fund by sub-Category type')
axes[3].set_title('Expense Ratio of Other fund by sub-Category type')
axes[4].set_title('Expense Ratio of Debt fund by sub-Category type')

plt.subplots_adjust(bottom=2.5,top=4)
plt.show()

Conclusion

Equity type -

  • Among equity type Sectoral and Dividend Yield funds have the highest expense ratio.

Hybrid type-

  • Among hybrid type Arbitrage Funds have the Lowest expense ratio.

Solution Oriented -

  • Chilren Funds have the highest expense ratio.

Other Fund-

  • FOFs Overseas has the highest expense Ratio.

Debt Fund-

  • Among debt fund Credit Risk Funds has the highest expense ratio.
In [33]:
# expense ratio vs rating

sns.boxplot(df,x='rating',y='expense_ratio')
plt.title('Variation of expense ratio across rating')
plt.show()

Findings -

  • For higher rating funds, expense ratio is less.
  • We will check category distribution with rating to learn more.
In [34]:
# Lets find why 0 rating fund has high expense ratio
pd.crosstab(df['rating'],df['category'],margins=True)
Out[34]:
category Debt Equity Hybrid Other Solution Oriented All
rating
0 4 78 2 19 5 108
1 22 18 10 4 4 58
2 66 54 25 14 9 168
3 96 76 39 27 5 243
4 64 52 27 12 3 158
5 30 30 13 4 2 79
All 282 308 116 80 28 814

Findings

  • Clearly 0 rating fund has more equity category=72% and equity fund has high expense ratio.
In [35]:
# expense ratio vs risk level

sns.boxplot(df,x='risk_level',y='expense_ratio')
plt.title('Variation of expense ratio across risk level')
plt.xticks(rotation='vertical')
plt.show()

Conclusion

  • Expense ratio is high for very high risk. We can check which category mostly falls in high risk as it has to be Equity.
In [36]:
# Lets find why very high risk fund has high expense ratio
pd.crosstab(df['risk_level'],df['category'],margins=True)
Out[36]:
category Debt Equity Hybrid Other Solution Oriented All
risk_level
High 3 0 7 15 2 27
Low 32 0 21 0 0 53
Moderate 109 0 9 4 2 124
Moderately High 16 8 29 6 6 65
Moderately Low 121 1 4 1 0 127
Very High 1 299 46 54 18 418
All 282 308 116 80 28 814

Findings

  • Most of the Very high risk funds are from Equity and equity fund has high expense ratio.

Analysis on fund size¶

In [37]:
# distribution of fund size
sns.histplot(df['fund_size_cr'])
plt.title('Fund Size Distribution')
plt.show()

Findings

  • Most of the fund size is less than 2000 Cr.
In [38]:
# fund size across fund age

#plt.figure(figsize=(12,8))
sns.boxplot(df,x='fund_size_cr',y='fund_age_yr',orient='h')
plt.title('Fund size variations with fund age')
plt.show()

Conclusion

  • 10 years old fund has maximum fund size.
In [39]:
# fund size vs rating 

#plt.figure(figsize=(12,8))
sns.boxplot(df,x='fund_size_cr',y='rating',orient='h')
plt.title('Fund size variation with the rating')
plt.show()

Conclusion

  • People seems to be interested in investing higher rated funds. We will see if ratings have any impact on returns in further slides.
In [40]:
# fund size vs risk level

#plt.figure(figsize=(12,8))
sns.boxplot(df[df['fund_size_cr']<10000],x='fund_size_cr',y='risk_level',orient='h')
plt.title('Fund size variations with Risk level')
plt.show()

Conclusion

  • Most of the funds are invested in risk level High and Very High
In [41]:
# top 10 AMC by sum of funds managed
df.groupby('amc_name')['fund_size_cr'].sum().sort_values(ascending=False).head(10).plot(kind='barh')
plt.ylabel('Total Funds managed in Crore')
plt.title('Funds managed by AMC across all schemes')
plt.show()

Conlusion:

  • ICICI Prudential Mutual Fund has received the highest funds collectively. (Popular AMC)
In [42]:
# category wise fund 
plt.figure(figsize=(10,8))
sns.boxplot(df,y='category',x='fund_size_cr')
plt.title('Funds variation across category')
plt.show()

Conlusion

  • Most of the funds are invested in Equity funds. (Popular category Debt and Equity)
In [43]:
# sub category wise fund

fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(10, 8),sharex='col')

sns.boxplot(equity_df,y='sub_category',x='fund_size_cr',ax=axes[0])
sns.boxplot(hybrid_df,y='sub_category',x='fund_size_cr',ax=axes[1])
sns.boxplot(solution_df,y='sub_category',x='fund_size_cr',ax=axes[2])
sns.boxplot(other_df,y='sub_category',x='fund_size_cr',ax=axes[3])
sns.boxplot(debt_df,y='sub_category',x='fund_size_cr',ax=axes[4])

axes[0].set_title('Fund size of Equity fund by sub-Category type')
axes[1].set_title('Fund size of Hybrid fund by sub-Category type')
axes[2].set_title('Fund size of Solution Oriented fund by sub-Category type')
axes[3].set_title('Fund size of Other fund by sub-Category type')
axes[4].set_title('Fund size of Debt fund by sub-Category type')

plt.subplots_adjust(bottom=10, top=12)
plt.show()

Conclusion

Equity-

  • Mid cap, Flexi Cap and Contra get more funds. (Popular Sub-category in Equity)

Hybrid-

  • Balanced and Arbitrage get more funds. (Popular Sub-category in Hybrid)

Debt-

  • Money market and Liquid mutual funds get more funds. (Popular Sub-category in Debt)
In [44]:
# schemes with high fund also has high returns (schemes giving higher returns will lead to funds inflow)

sns.catplot(df,y='fund_size_group',x='returns_5yr',col='category',col_wrap=3,kind='bar')
plt.subplots_adjust(top=0.8)
plt.suptitle('Returns variations with Fund size',fontsize=15)
plt.show()

Conclusion

  • Except Debt category all other are funds returns are related to fund size.
  • We found that as fund size increases expense ratio decreases

Analysis on fund age¶

In [45]:
# distribution of fund age
sns.histplot(df['fund_age_yr'])
plt.title('Distribution of Fund Age')
plt.show()
In [46]:
sns.histplot(df,x='fund_age_yr',hue='category',kde=True)
plt.title('Distribution of Fund Age')
plt.show()

Findings

  • Recent funds are mostly Debt funds
  • More than 4 year age funds are Equity type followed by Debt.
In [47]:
# can we say that new funds give better returns ?

sns.catplot(df,x='returns_5yr',y='fund_age_yr',orient='h',kind='box',col='category',col_wrap=3)
plt.subplots_adjust(top=0.8)
plt.suptitle('Returns variations with Fund age',fontsize=15)
plt.show()

Conclusion

  • It seems there is no dependency of returns on fund age. Though older funds have more data which we can analyse before investing.

Analysis on risk level¶

In [48]:
# total no of schemes in each risk level
sns.countplot(df,x='risk_level')
plt.title('Number of funds across Risk level')
plt.ylabel('Number of funds')
plt.xticks(rotation='vertical')
plt.show()

Conclusion

  • Most of the schemes have risk level Very High
In [49]:
# amc and risk level
plt.figure(figsize=(14,10))
sns.heatmap(pd.crosstab(df['amc_name'],df['risk_level'],normalize='index'),annot=True,fmt='.1%')
plt.title('AMC wise schemes distribution across risk level in percentage')
plt.show()

Conclusion

  • Most of the AMCs have schemes with risk level 6(Very High).
  • Taurus, Shriram and L&T have 100% in Very High Risk category.
In [50]:
# category and risk level
sns.heatmap(pd.crosstab(df['category'],df['risk_level'],normalize='index'),annot=True,fmt='.1%')
plt.title('Schemes distribution across category and risk level')
plt.show()

Findings

  • Low, Moderate and Moderately Low are dominated by Debt Funds
  • Moderately High, High and very High are dominated by Equity Funds
In [51]:
# rating and risk level
sns.heatmap(pd.crosstab(df['rating'],df['risk_level'],normalize='index'),annot=True,fmt='.1%')
plt.title('Schemes distribution across rating and risk level')
plt.show()

Findings -

  • 0 rating funds have major concentration of Very High risk funds.
In [52]:
sns.heatmap(pd.pivot_table(data=df,index='rating',columns='risk_level',values='returns_5yr'),annot=True,fmt='.3g')
plt.title('Variations of 5 years returns across rating and risk level')
plt.show()

Findings -

  • 0 rating funds has high return as in the above cell you can see most 0 rated funds are in equity.
  • For risk level 5 and 6 as high rated fund should be chosen as returns are 13.4% and 15.4% respectively.
In [53]:
# high risk level means high returns ??

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(df,x='risk_level',y='returns_1yr',ax=axes[0])
sns.barplot(df,x='risk_level',y='returns_3yr',ax=axes[1])
sns.barplot(df,x='risk_level',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year')
axes[0].set_xticklabels(rotation='vertical',labels=df['risk_level'].unique())
axes[1].set_title('Returns over 3 year')
axes[1].set_xticklabels(rotation='vertical',labels=df['risk_level'].unique())
axes[2].set_title('Returns over 5 year')
axes[2].set_xticklabels(rotation='vertical',labels=df['risk_level'].unique())

plt.tight_layout()
plt.show()

Findings -

  • In 3 and 5 years returns have a positive relation with risk level.
  • It clearly shows that high risk funds should be held for longer time to get more returns.

Analysis on AMC¶

In [54]:
# distribution of amc
plt.figure(figsize=(10,8))
sns.countplot(df,y='amc_name')
plt.title('Number of schemes vs AMC')
plt.xlabel('Number of schemes')
plt.show()

Findings- ICICI has around 57 schemes running

In [55]:
# avg rating of amc funds
plt.figure(figsize=(10,8))
sns.barplot(df,y='amc_name',x='rating')
plt.title('Avg rating across AMC')
plt.xlabel('Avg rating')
plt.show()

Findings - Quant Mutual Funds have the highest rating.

In [56]:
# amc name vs category
plt.figure(figsize=(10,8))
sns.heatmap(pd.crosstab(df['amc_name'],df['category'],normalize='index'),annot=True,fmt='.1%')
plt.title('Percent of schemes by AMC across category')
plt.show()

Findings -

  • Most of the AMC have funds in Debt and Equity category.
In [57]:
plt.figure(figsize=(10,8))
sns.heatmap(pd.pivot_table(df,index='amc_name',columns='category',values='returns_5yr'),annot=True,fmt='.2g')
plt.title('Avg of 5 years returns by AMC across category')
plt.show()

Findings-

  • Highest return of 13% in debt category is given by Navi Mutual Fund. (Though there was 1 debt fund by Navi)
  • Highest return of 19% in Equity category is given by Quant Mutual Fund.
  • Highest Return of 20% in Hybrid category is given by Quant Mutual Fund.
In [58]:
# amc name vs sub-category
plt.figure(figsize=(22,20))
sns.heatmap(pd.crosstab(df['amc_name'],df['sub_category']),annot=True)
plt.title('Number of schemes by AMC across sub-category')
plt.show()

Findings

  • Most of schemes are opened in Sectoral/Thematic sub category.
In [59]:
plt.figure(figsize=(14,10))
sns.heatmap(pd.pivot_table(df,index='amc_name',columns='sub_category',values='returns_5yr'),annot=True,fmt='.2g')
plt.title('Avg 5 years of Returns given by AMC across different sub-category')
plt.show()

Findings - Quant Mutual Funds have performed very well across different Sub categories.

Analysis of Rating¶

In [60]:
df['rating'].value_counts().plot(kind='bar')
plt.title('Distribution of Rating')
plt.show()
In [61]:
# does returns vary because of ratings ??

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(df,x='rating',y='returns_1yr',ax=axes[0])
sns.barplot(df,x='rating',y='returns_3yr',ax=axes[1])
sns.barplot(df,x='rating',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year')
axes[1].set_title('Returns over 3 year')
axes[2].set_title('Returns over 5 year')

plt.tight_layout()
plt.show()

Findings -

  • Except for 0 rating there is direct correlation in returns. As only in 0 rating, Equity funds are more than Debt funds
  • Leaving 0 rating category, we can say that higher the rating higher returns.
In [62]:
# lets check why 0 rating is giving higher returns
sns.heatmap(pd.crosstab(df['rating'],df['category'],normalize='index'),annot=True,fmt='.1%')
Out[62]:
<Axes: xlabel='category', ylabel='rating'>

Findings

  • Equity constitutes 72% in 0 rating
  • Other ratings has somewhat equal distribution.
In [63]:
plt.figure(figsize=(14,10))
sns.heatmap(pd.pivot_table(df,index='rating',columns='category',values='returns_5yr'),annot=True,fmt='.2g')
plt.title('Avg 5 years of Returns given by Rating and Category')
plt.show()

Findings

  • Leaving 0 rating category, we can say that higher the rating higher returns.
In [64]:
sns.barplot(df,x='category',y='rating')
plt.title('Avg Rating across category')
plt.show()

Analysis on category¶

In [65]:
# distribution of category
sns.countplot(df,x='category')
plt.title('Number of schemes across category')
plt.ylabel('Number of schemes')
plt.show()

Findings - Among category most of the schemes are Equity or Debt type

In [66]:
sns.barplot(df,x='category',y='returns_5yr')
plt.title('Avg 5 years returns across category')
plt.ylabel('Average of 5 years returns')
plt.show()

Findings - Equity has given highest return

In [67]:
# sub category vs category

plt.figure(figsize=(8,10))
sns.heatmap(pd.crosstab(df['sub_category'],df['category']),annot=True,fmt='g')
plt.title('Number of schemes across sub-category by category ')
plt.show()

Findings:

  • Most of the debt funds is Liquid Mutual Funds
  • Most of the Equity funds is Sectoral/Thematic
  • Most of the funds in Hybrid category is Aggressive Hybrid Mutual funds
  • Most of the Other category funds is FOFs domestic
  • Solution Oriented fund includes only Retirement funds
In [68]:
# category wise returns

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4),sharey='row')

# Create a bar plot for each axis
sns.barplot(df,x='category',y='returns_1yr',ax=axes[0])
sns.barplot(df,x='category',y='returns_3yr',ax=axes[1])
sns.barplot(df,x='category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year')
axes[0].set_xticklabels(df['category'].unique(),rotation='vertical')
axes[1].set_title('Returns over 3 year')
axes[1].set_xticklabels(df['category'].unique(),rotation='vertical')
axes[2].set_title('Returns over 5 year')
axes[2].set_xticklabels(df['category'].unique(),rotation='vertical')

plt.show()

Findings

  • Debt has been good in 1 year returns
  • For 3 and 5 year Equity has outperformed other categories.

Analysis of sub category¶

In [69]:
# distribution of sub category
plt.figure(figsize=(10,8))
sns.countplot(df,y='sub_category')
plt.title('Number of schemes by sub-category')
plt.xlabel('Number of Schemes')
plt.show()

Findings

  • Most popular sub-category are Sectoral/Thematic and ELSS Mutual Funds
In [70]:
# returns of debt category and various sub category over 1 year, 3 year, 5 year

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(debt_df,x='sub_category',y='returns_1yr',ax=axes[0])
sns.barplot(debt_df,x='sub_category',y='returns_3yr',ax=axes[1])
sns.barplot(debt_df,x='sub_category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year for debt funds')
axes[0].set_xticklabels(debt_df['sub_category'].unique(),rotation='vertical')
axes[1].set_title('Returns over 3 year for debt funds')
axes[1].set_xticklabels(debt_df['sub_category'].unique(),rotation='vertical')
axes[2].set_title('Returns over 5 year for debt funds')
axes[2].set_xticklabels(debt_df['sub_category'].unique(),rotation='vertical')

plt.tight_layout()
plt.show()

Findings

  • Over 1 year best sub-category in Debt Category is Credit Risk Funds
  • Over 3 year best sub-category in Debt Category is Fixed Maturity Plans
  • Over 5 year best sub-category in Debt Category is Fixed Maturity Plans
In [71]:
# returns of equity category and various sub category over 1 year, 3 year, 5 year

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(equity_df,x='sub_category',y='returns_1yr',ax=axes[0])
sns.barplot(equity_df,x='sub_category',y='returns_3yr',ax=axes[1])
sns.barplot(equity_df,x='sub_category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year for equity fund')
axes[0].set_xticklabels(equity_df['sub_category'].unique(),rotation='vertical')
axes[1].set_title('Returns over 3 year for equity fund')
axes[1].set_xticklabels(equity_df['sub_category'].unique(),rotation='vertical')
axes[2].set_title('Returns over 5 year for equity fund')
axes[2].set_xticklabels(equity_df['sub_category'].unique(),rotation='vertical')

plt.tight_layout()
plt.show()

Findings

  • Over 1 year best sub-category in Equity Category is Contra Funds
  • Over 3 year best sub-category in Equity Category is Small Cap Funds
  • Over 5 year returns do not differ much.
In [72]:
# returns of hybrid category and various sub category over 1 year, 3 year, 5 year

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(hybrid_df,x='sub_category',y='returns_1yr',ax=axes[0])
sns.barplot(hybrid_df,x='sub_category',y='returns_3yr',ax=axes[1])
sns.barplot(hybrid_df,x='sub_category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year for Hybrid Fund')
axes[0].set_xticklabels(hybrid_df['sub_category'].unique(),rotation='vertical')
axes[1].set_title('Returns over 3 year for Hybrid Fund')
axes[1].set_xticklabels(hybrid_df['sub_category'].unique(),rotation='vertical')
axes[2].set_title('Returns over 5 year for Hybrid Fund')
axes[2].set_xticklabels(hybrid_df['sub_category'].unique(),rotation='vertical')

plt.tight_layout()
plt.show()

Findings

  • Over 1 year best sub-category in Hybrid Category is Arbitrage and Conservative Funds
  • Over 3 year best sub-category in Hybrid Category is Aggressive and Multi Asset Allocation
  • Over 5 year best sub-category in Hybrid Category is Aggressive and Multi Asset Allocation
In [73]:
# returns of other category and various sub category over 1 year, 3 year, 5 year

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(other_df,x='sub_category',y='returns_1yr',ax=axes[0])
sns.barplot(other_df,x='sub_category',y='returns_3yr',ax=axes[1])
sns.barplot(other_df,x='sub_category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year for Other Funds')
axes[1].set_title('Returns over 3 year for Other Funds')
axes[2].set_title('Returns over 5 year for Other Funds')

plt.tight_layout()
plt.show()

Findings

  • Over 1 year best sub-category in Other Category is FoFs Domestic, also FoFs overseas has given negative return
  • Over 3 year best sub-category in Other Category is Index Funds
  • Over 5 year best sub-category in Other Category is FoFs Domestic and Index Funds
In [74]:
# returns of solution oriented category and various sub category over 1 year, 3 year, 5 year


# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a bar plot for each axis
sns.barplot(solution_df,x='sub_category',y='returns_1yr',ax=axes[0])
sns.barplot(solution_df,x='sub_category',y='returns_3yr',ax=axes[1])
sns.barplot(solution_df,x='sub_category',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year for Solution Funds')
axes[1].set_title('Returns over 3 year for Solution Funds')
axes[2].set_title('Returns over 5 year for Solution Funds')

plt.tight_layout()
plt.show()

Findings

  • Over 1 year best sub-category in Solution Category is Retirement Funds
  • Over 3 year best sub-category in Solution Category is Children Funds
  • Over 5 year returns do not differ much

Analysis on alpha¶

Alpha is defined as the excess return of the mutual fund over the benchmark return, on a risk-adjusted basis.

Risk-adjusted basis means we need to –

Calculate the difference between the mutual fund returns and the risk-free return. Calculate the difference between the benchmark return and the risk-free return, multiply this by the beta. Take the difference between 1 and 2

Mathematically,

Alpha = (MF Return – riskfree return) – (Benchmark return – riskfree return)*Beta

Lets put this in context with an example. Assume a certain fund gives you a return of 10%, its benchmark returns for the same duration is 7%. The beta of the fund is 0.75. What do you think the alpha assuming the risk-free rate is 4%?

Let’s apply the for formula and check –

Alpha = (10%-4%)-(7%-4%)*0.75 = 6% – 2.25% = 3.75% or 0.0375

As you can see, the alpha is not just the difference between the fund and its benchmark, which if true, the alpha would have been – 10% – 7% =3%

But rather, the alpha is 3.75%.

The fund has managed to generate a 10% return compared to the Index’s 7% while managing to stay significantly less volatile (remember beta is just 0.75). Hence we are rewarding the fund for its good behaviour or less volatile behaviour. Therefore the alpha is 3.75% as opposed to just 3%.

Now, imagine the same fund, with the same returns, but the beta is 1.3 instead of 0.75. What do you think is the alpha? By now, you should guess that since the beta is high, the fund gets penalised for its erratic behaviour. Therefore the alpha should be lower.

Let us see if the numbers agree to this thought.

Alpha = (10%-4%)-(7%-4%)*1.3 = 6% – 3.9% = 2.1%

See that? While the returns remain the same, thanks to beta, the alpha is significantly lesser on a risk-adjusted basis.

To conclude, alpha is the excess return of the fund over above the benchmark returns. Alpha is a risk-adjusted. The fund is rewarded if the returns are generated by keeping a low-risk profile and penalized for being volatile.

By now, you must have realized that volatility plays an important role in measuring mutual funds performance. Beta is a measure of volatility; it tells us how risky the fund is when compared to its benchmark. Beta is a relative risk and does not reveal the fund’s inherent risk.

The inherent risk of a fund is revealed by the ‘Standard Deviation’ of the fund.

In [75]:
sns.histplot(df['alpha'])
plt.title('Distribution of Alpha')
plt.show()
In [76]:
# high alpha value has higher returns 

# Create figure and axis objects
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 6),sharey='row')

# Create a scatter plot for each axis
sns.scatterplot(df,x='alpha',y='returns_1yr',ax=axes[0])
sns.scatterplot(df,x='alpha',y='returns_3yr',ax=axes[1])
sns.scatterplot(df,x='alpha',y='returns_5yr',ax=axes[2])

# Set the title for each axis
axes[0].set_title('Returns over 1 year')
axes[1].set_title('Returns over 3 year')
axes[2].set_title('Returns over 5 year')

plt.tight_layout()
plt.show()

Findings

  • Returns are higher for high Alpha
In [77]:
# alpha vs category
sns.barplot(df,x='category',y='alpha')
plt.title('Alpha variation across category')
plt.show()

Findings

  • Equity has highest Alpha
In [78]:
# alpha vs risk
sns.barplot(df,y='risk_level',x='alpha')
plt.title('Alpha variation across risk')
plt.show()

Findings

  • Moderately high risk has high Alpha
In [79]:
# alpha vs rating
sns.barplot(df,x='rating',y='alpha')
plt.title('Alpha variation across category')
plt.show()

Finding

  • 5 Rated fund has high Alpha
  • Again since 0 rated fund has mostly Equity category and we have seen Equity category has high Alpha
In [80]:
sns.pairplot(df,vars=['alpha','sd','sortino','beta','sharpe'],kind='reg')
Out[80]:
<seaborn.axisgrid.PairGrid at 0x1e4e0cb75b0>

Findings

  • Sharpe and Alpha are positively correlated.
  • Sortino and Sharpe are positively correlated.
  • Sortino and Alpha are positively correlated.

Analysis of Standard deviation (sd)¶

The standard deviation of a stock or a mutual fund represents the riskiness of the stock or the mutual fund. Higher the standard Deviation, higher is the volatility of the asset. Higher the volatility, higher is the risk.

For example consider these 2 funds - The SD of the small-cap fund is 23.95% while the long term equity is 19.33%, which implies that the small-cap fund is way riskier compared to the long term equity fund.

To put this context, if you invest Rs.10,000/- across funds at the same time, then by the end of the year the profit or loss can be anywhere in this range –

Loss = Investment * (1-SD)

Gains = Investment * (1+SD)

image.png

The larger the SD, the larger the possibility of loss or gains.

Generally speaking, the SD for mid and small-cap funds are higher compared to large-cap stocks.

Do note, volatility or Standard Deviation should not worry you. Markets are volatile, and equities are volatile, mutual funds are volatile; this is the very nature of markets. So if you can’t fathom watching your investment see-saw between gains and loss, then perhaps you should reconsider your investment decision in equities.

But if you do invest in equities, then you need to learn to manage volatility. There are two ways to deal with this beast called ‘Volatility’ –

  • Diversify smartly (and not over diversify)
  • Give your investment time
In [81]:
sns.histplot(df['sd'])
plt.title('Distribution of SD')
plt.show()
In [82]:
# sd vs category
sns.barplot(df,x='category',y='sd')
plt.title('Standard deviation variation across category')
plt.show()

Findings

  • Equity has high SD means it is more volatile
  • Debt has less SD means it is less volatile.
In [83]:
# sd vs sub category
plt.figure(figsize=(10,8))
sns.barplot(df,y='sub_category',x='sd')
plt.title('Standard deviation variation across sub-category')
plt.show()

Findings

  • FoFs Overseas has highest volatility and it should be invested for long duration.
  • For short duration investment Liquid and Overnight funds are more suitable as they are less volatile.
In [84]:
# sd vs risk
sns.barplot(df,y='risk_level',x='sd')
plt.title('Standard deviation variation across Risk Level')
plt.show()

Findings

  • Very High risk category has high SD
In [85]:
# sd vs rating
sns.barplot(df,x='rating',y='sd')
plt.title('Standard deviation variation across rating')
plt.show()

Findings

  • Rating 0 and Rating 5 has most of the Equity Category hence higher SD

Analysis of beta¶

Beta = Covariance between mutual fund and benchmark / Variance of benchmark index

A beta of 1 indicates that the mutual fund has the same level of volatility as the benchmark index. A beta of less than 1 indicates lower volatility, while a beta of greater than 1 indicates higher volatility.

For example, the Quant Small Cap Fund has a beta of 0.92, hence the fund is slightly less risky compared to its benchmark. I say slightly because it’s very close to 1. This implies, if Nifty Small Cap 250 Index falls by 1%, then Quant Small Cap Fund is expected to fall by 0.92%.

If the beta was 0.6 or 0.65, the fund is less risk or less volatile compared to its benchmark. Why? Because if Nifty Small Cap 250 Index falls by 1%, then Quant Small Cap Fund is expected to fall by only 0.65% and not 0.95%.

This is what I mean by ‘relative risk’; it gives us a perspective of how risky the fund is compared to its benchmark.

When you are looking at the Beta of a stock or an MF, it is very important to recognize the fact that the beta is a measure of relative risk, it tells us how risky the stock or MF is compared to its benchmark. Beta is not an indicator of the inherent risk of the stock or MF.

To put this in context, think about it this way, Ferrari is faster compared to a BMW, this comparison is like the beta. We measure the speed of car one against car two. But does this give you any indication of how fast the Ferrari is? Not really.

Likewise, while beta gives us a perspective of the relative riskiness of an asset, it does not give us the absolute or the inherent risk of the asset itself.

By now, you must have built your perception of beta. Let me ask you this – if a mutual fund has a high beta, do you think it is bad?

Well, the good, bad, ugly part of beta depends on another metric called the ‘Alpha’.

To know more about Mutual Fund Metrics - refer to Zerodha varsity

In [86]:
sns.histplot(df['beta'])
plt.title('Distribution of Beta')
plt.show()
In [87]:
# category vs beta
sns.boxplot(df,x='category',y='beta')
plt.ylim(-5,5)
plt.title('Beta variation across category')
plt.show()

Findings

  • Debt funds has negative beta it means when market will go down then returns for these funds will increase and vice versa.
In [88]:
# equity mutual funds has high beta
sns.boxplot(equity_df,x='sub_category',y='beta',)
plt.xticks(rotation='vertical')
plt.ylim(0,2)
plt.title('Beta variation across Equity sub-category')
plt.show()
In [89]:
# debt mutual funds has high beta variations

sns.boxplot(debt_df,x='sub_category',y='beta')
plt.xticks(rotation='vertical')
plt.ylim(-5,7)
plt.title('Beta variation across Debt sub-category')
plt.show()
In [90]:
# beta vs risk
sns.barplot(df,y='risk_level',x='beta')
plt.title('Beta variation across Risk Level')
plt.show()
In [91]:
# beta vs rating
sns.barplot(df,x='rating',y='beta')
plt.title('Beta variation across Rating')
plt.show()

Analysis on Sharpe¶

Assume, there are two large-cap funds -Fund A and Fund B. Here is how they have performed in terms of returns –

Fund A – 14%

Fund B – 16%

Which of the two funds are better? Well, Fund B has a higher return, so without a doubt, Fund B is a better fund.

Now, consider the following –

image.png

Rf is the risk-free return. Along with the fund’s return, I’ve also stated the standard deviation/volatility/risk of the two funds. Now, which of the two funds do you think is better?

I guess it gets a little complex to figure out which these two funds are better given that we have to evaluate them on two parameters, i.e. both the risk and return.

Ignoring the risk, purely on a return basis, Fund B is better. Ignoring the return, purely on a risk basis, Fund A is better. But in reality, you cannot isolate risk and reward; you need to factor in both these and figure out which of these two are better.

The Sharpe Ratio helps us here. It bundles the concept of risk, reward, and the risk-free rate and gives us a perspective.

Sharpe ratio = [Fund Return – Risk-Free Return]/Standard Deviation of the fund

Lets apply the math for Fund A –

= [14% – 6%] / 28%

= 8%/28%

= 0.29

The number tells us that the fund generates 0.29 units of return (over and above the risk-free return) for every unit of risk undertaken.Naturally, by this measure, the higher the Sharpe ratio, the better it is as we all want higher returns for every unit of risk undertaken.

Lets see how this turns out for Fund B –

= [16% – 6% ] / 34%

= 10% / 34%

= 0.29

So it turns out that both the funds are similar in terms of their risk and reward perspective. And there is no advantage of choosing Fund A over Fund B.

Do note, Sharpe ratio considers only price based risk. It does not consider credit or interest rate risk. Hence, there is no point looking at the Sharpe ratio for debt funds.

In [92]:
sns.histplot(df['sharpe'])
plt.title('Distribution of Sharpe')
plt.show()
In [93]:
# category vs sharpe
sns.barplot(df,x='category',y='sharpe')
plt.title('Variation of Sharpe ratio across category')
plt.show()

Findings

  • Equity fund has highest Sharpe Ratio
In [94]:
# sharpe vs risk
sns.barplot(df,y='risk_level',x='sharpe')
plt.title('Sharpe variation across risk level')
plt.show()

Findings

  • Very High and Moderately High has somewhat same average Sharpe
In [95]:
# sharpe vs rating
sns.barplot(df,x='rating',y='sharpe')
plt.title('Sharpe variation across rating')
plt.show()

Findings

  • As rating increases sharpe ratio also increases

Analysis on Sortino¶

The Sortino’s ratio is an improvisation over the Sharpe Ratio, wherein the denominator has only the negative returns or the ‘downside risk’, is considered.

Hence, the Sortino’s Ratio is –

= [Fund Return – Risk-Free Return]/Downside Risk

The objective of Sortino’s ratio is to estimate the excess return adjusted for only the downside risk. Like the Sharpe ratio, higher the Sortino’s ratio, better it is.

In [96]:
sns.histplot(df['sortino'])
plt.title('Distribution of Sortino')
plt.show()
In [97]:
# category vs sortino
sns.barplot(df,y='category',x='sortino')
plt.title('Variation of Sortino ratio across category')
plt.show()

Findings

  • Equity has highest Sortino
In [98]:
# sortino vs risk level
sns.barplot(df,y='risk_level',x='sortino')
plt.title('Sortino variation across risk level')
plt.show()

Findings

  • Very High Risk category has high sortino
In [99]:
# sortino vs rating
sns.barplot(df,x='rating',y='sortino')
plt.title('Sortino variation across rating')
plt.show()

Finding:

  • Rating and sortino are positively correlated
In [100]:
sns.histplot(df[df['returns_1yr']<30],x='returns_1yr',hue='category',kde=True)
plt.title('Distribution of 1 year returns across category')
plt.show()

Findings

  • For 1 year investment Debt fund has performed well. Other category may give negative returns as well.
In [101]:
sns.histplot(df,x='returns_3yr',hue='category',kde=True)
plt.title('Distribution of 3 year returns across category')
plt.show()

Findings

  • Equity has outperformed every other category for 3 years performance.
In [102]:
sns.histplot(df,x='returns_5yr',hue='category',kde=True)
plt.title('Distribution of 5 year returns across category')
plt.show()

Findings

  • Equity has outperformed every other category for 5 years performance.
In [103]:
# top schemes in each category and sub category in terms of returns earned

category_idx = df.groupby("category")["returns_5yr"].idxmax()
category_result = df.loc[category_idx, ["category", "scheme_name", "returns_5yr"]]
In [104]:
category_result
Out[104]:
category scheme_name returns_5yr
63 Debt AXIS FTP – Series 104 – 1112Days 14.0
608 Equity Quant Small Cap Fund 23.2
607 Hybrid Quant Multi Asset Fund 20.5
456 Other Kotak Multi Asset Allocator FoF – Dynamic – Di... 15.3
252 Solution Oriented HDFC Retirement Savings Fund 14.8
In [105]:
sub_cat_idx = df.groupby("sub_category")["returns_5yr"].idxmax()
sub_catresult = df.loc[sub_cat_idx, ["sub_category", "scheme_name", "returns_5yr"]]
In [106]:
sub_catresult
Out[106]:
sub_category scheme_name returns_5yr
599 Aggressive Hybrid Mutual Funds Quant Absolute Fund 19.100000
86 Arbitrage Mutual Funds Bank of India Arbitrage Fund 8.581053
182 Banking and PSU Mutual Funds Edelweiss Banking & Psu Debt Fund 7.900000
226 Childrens Funds HDFC Children’s Gift Fund 12.900000
436 Conservative Hybrid Mutual Funds Kotak Debt Hybrid Fund 10.000000
623 Contra Funds SBI Contra Fund 15.600000
267 Corporate Bond Mutual Funds HSBC Corporate Bond Fund 13.300000
306 Credit Risk Funds ICICI Pru Credit Risk Fund 8.100000
753 Dividend Yield Funds Templeton India Equity Income Fund 14.500000
223 Dynamic Asset Allocation or Balanced Advantage HDFC Balanced Advantage Fund 12.900000
275 Dynamic Bond HSBC Flexi Debt Fund 13.300000
609 ELSS Mutual Funds Quant Tax Plan- Direct Growth 22.200000
487 Equity Savings Mutual Funds Mahindra Manulife Equity Savings Fund 13.300000
63 Fixed Maturity Plans AXIS FTP – Series 104 – 1112Days 14.000000
601 Flexi Cap Funds Quant Flexi Cap Fund 18.200000
313 Floater Mutual Funds ICICI Pru Floating Interest Fund 7.200000
456 FoFs Domestic Kotak Multi Asset Allocator FoF – Dynamic – Di... 15.300000
178 FoFs Overseas DSP World Mining Fund 15.000000
371 Focused Funds IIFL Focused Equity Fund 18.100000
157 Gilt Mutual Funds DSP G-Sec Fund 8.600000
556 Index Funds Nippon India Index Fund 13.000000
501 Large & Mid Cap Funds Mirae Asset Emerging Bluechip Fund 15.100000
126 Large Cap Mutual Funds Canara Robeco Bluechip Equity Fund 14.400000
413 Liquid Mutual Funds ITI Liquid Fund 6.385446
32 Low Duration Funds Aditya Birla SL Low Duration Fund 7.000000
34 Medium Duration Funds Aditya Birla SL Medium Term Plan – Direct Growth 8.600000
555 Medium to Long Duration Funds Nippon India Income Fund 7.900000
606 Mid Cap Mutual Funds Quant Mid Cap Fund 19.800000
193 Money Market Funds Edelweiss Money Market Fund 7.300000
607 Multi Asset Allocation Mutual Funds Quant Multi Asset Fund 20.500000
600 Multi Cap Funds Quant Active Fund 19.900000
812 Overnight Mutual Funds WhiteOak Capital Overnight Fund 13.300000
252 Retirement Funds HDFC Retirement Savings Fund 14.800000
715 Sectoral / Thematic Mutual Funds Tata Digital India Fund 22.100000
345 Short Duration Funds ICICI Pru Short Term Fund 7.700000
608 Small Cap Mutual Funds Quant Small Cap Fund 23.200000
538 Ultra Short Duration Funds Navi Ultra Short Term Fund 13.300000
351 Value Funds ICICI Pru Value Discovery Fund 14.900000

This notebook is for Educational purpose only and one should check with there advisor before investing in mutual funds.